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According to Industry 4.0, real-time information in production planning and control, shows a high potential for optimizing the whole supply chain. The paper considers the plant building industry, especially the off-site fabrication and on-site installation. Traditionally, production planning is centralized following a Master Schedule that rarely is...
Contexts in source publication
Context 1
... of the first tier supplier [24]. As a result, ETO-components are fabricated and delivered to the site for installation according to a static Master Schedule and not according to the real demand. Static means in this case that the schedule is not frequently updated and therefore it does not reflect deviations throughout the supply chain. In Fig. 1, the schematic depiction of a traditionally first-tier ETO supply chain in the plant building and construction industry is visualized. Usually, one fabrication shop has to supply ETO-components to different installation sites. In this case, as an abstracted example, the first-tier supplier delivers to four different installation sites. ...
Context 2
... chain. The Pitch is defined per task and it specifies how much construction progress (e.g. how many rooms) should be completed by a specific crew in a given time period (e.g. day or week) on the installation site. According to this indication the amount of needed components on-site, as well as the needed shop floor drawings are determined. In Fig. 1, the Master Schedule is configured in a weekly granularity level. As a result, in every calendar week (CW), one Pitch is installed at every installation site (1 to 4) and every three CWs an amount of components of three Pitches exits the fabrication shop of the first-tier supplier. The supply chain is coordinated by means of a ...
Context 3
... visualized in Fig. 1, the traditionally centralized production planning and control suffers from a missing feedback loop between the customer (installation site) and the supplier. More in detail, deviations of the different installation sites are not considered in real-time in the Master Schedule, which leads to the following types of waste: 1) ...
Context 4
... of 'Release 1' in Fig. 2. As soon as CW 1 is finished, the performed tasks are recorded and a next planning for CW 2 until CW 4 is done. This process repeats until CW n. Because the Engineering department needs a long preliminary lead-time, it is organized according to the Master Schedule. As different from the traditional approach visualized in Fig. 1, production planning in the fabrication shop is organized according to the order backlog list, which is prioritized in real-time based on different customer requests (Cyclically Planning on the installation sites). More in detail, the prioritization of Pitches (amount of components) to be delivered to the different installation sites ...
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Citations
... CPS capture data from the physical world via sensors, use the internet and cloud computing to communicate between the connectors and interact with the physical world using mechatronic actuators (Lee 2008;Zsifkovits and Woschank 2019). This enables autonomous control systems, which can satisfy customer demands in real-time (Spath et al. 2013;Dallasega et al. 2017). CPS, as well as the Internet of Things (IoT), allow enterprises to sense deviations from the schedule as soon as they appear (Magoutas, et al. 2014;Chaopaisarn and Woschank 2019). ...
In recent years, production planning and control strategies have gained increasing importance within manufacturing enterprises to stay competitive by enhancing the ability to meet and quickly adapt to the hyper-dynamic requirements of highly volatile markets. In this context, traditional production planning and control strategies like material requirements planning often result in long lead times and high work in progress due to their weak responsiveness to short-term fluctuations in demand. However, new production planning and control strategies and concepts based on the usage of real-time data, as a fundamental principle of Industry 4.0, may have the potential to compensate for the shortcomings of traditional approaches. By using a discrete event simulation based on the data of an electronics manufacturing company, the potential of using real-time data to increase the update frequency in different production planning and control strategies in make to order production systems is analyzed.
... CPS and IoT are combined in Industry 4.0 to the industrial automation domain (Ben-Daya et al., 2017). Industry 4.0 transforms factories into smart factories including all components like devices, suppliers, products, logistics, manufacturing processes, engineering, machines, etc. (Shrouf et al., 2014;Schmidt et al., 2015;Ardito et al., 2017;Ben-Daya et al., 2017;Dallasega et al., 2017;Kamble et al., 2018;Tuptuk and Hailes, 2018;. Industry 4.0 promotes cloud manufacturing, the Internet of Things, big data, robotics, the Internet of Services (IoS), and CPS. ...
... Industry 4.0 promotes cloud manufacturing, the Internet of Things, big data, robotics, the Internet of Services (IoS), and CPS. As a result, all the types of equipment including machines, devices, also, production modules and products, etc. are used in a variety of fields, including management, manufacturing, supply chain, especially in real-time situations (Kang et al., 2016;Dallasega et al., 2017;Pereira and Romero, 2017;Melnyk et al., 2018;Yli-Ojanperä et al., 2019;. To learn more about CPS, interested readers are referred here (Chen, 2017;Lu, 2017;Trstenjak and Cosic, 2017;Gürdür and Asplund, 2018;Li, 2018;Zhang et al., 2019). ...
... Three types of effects between variables are calculated, which are [59,67]: ...
... For the Planning → Execution relationship in H1, it is concluded that there is sufficient evidence to state that the Planning stage has a direct and positive effect on the Execution stage since when the first variable increases its standard deviation by one unit, the second increases it by 0.76 units and can explain 58% of its variability. The above indicates that the investment and training in ICT in the SC favors the operations management, information exchange, production control, and activities associated with product distribution, which agrees with Dallasega et al. [67]. That finding indicates that managers should plan the ICT implementation processes in the SC, in order to understand the activities to be carried out, the dates, and who is responsible for the execution; 2. ...
This paper reports a second order structural equation model (SEM) with four latent variables and six hypotheses to analyze the Planning, Execution, and Control of the information and communication technologies (ICT) implementation in supply chains (SC) and the operational Benefits obtained. The model is validated with information obtained from 80 responses to a questionnaire applied direct to manufacturing companies in Baja California state (Mexico), specifically in Ensenada, Mexicali, Tecate, and Tijuana municipalities. The variables are statistically validated using the Cronbach’s alpha index for internal and R-squared for predictive validity. Partial least squares algorithms are used to validate the model’s hypotheses in software WarpPLS version7.0 ScripWarp Systems, Laredo, TX, US. Findings indicate that the direct impact of Execution and Control is positive and therefore are the basis for successful integration of ICT and obtaining agility and flexibility benefits in the SC.
... A strand of studies on this theme has emphasized I4.0driven changes, such as demands of mass customization and the constant stream of data generation, that entail volatility and constant change in PS solutions [5,36]. Another group of scholars has examined rescheduling strategies based on real-time scheduling approaches, such as AI-based heuristics [13], optimization algorithms [24], and simulations [7,8]. These approaches addressed job arrivals, machine breakdown, and inventory-related uncertainties typical of rescheduling. ...
... • Mobile phone chip [27] • Construction component suppliers [7] • Off-site plant building [8] • Auction logistics center [ • Multi-agent systems for job-shop scheduling [29] • Hybrid scheduling algorithms for task management [27] • Particle swarm optimization metaheuristic [50] • Cloud-based auction logistics service [25] • AM technologies mass customization [ • ETO construction lead time reduction [7] • Collaborative robots for shop-floor tasks [57] • Reduction in machine starvation and production losses [22] • Reduction in machinedependent electricity costs [37] • Agile scheduling in ETO construction [8] • Rescheduling efficiency [13] • PS recovery models for disruption management [19] Financial/ Market performance outcomes V23: Service levels V24: Cost of nonconformance V25: Quality ...
... • Mobile phone chip [27] • Construction component suppliers [7] • Off-site plant building [8] • Auction logistics center [ • Multi-agent systems for job-shop scheduling [29] • Hybrid scheduling algorithms for task management [27] • Particle swarm optimization metaheuristic [50] • Cloud-based auction logistics service [25] • AM technologies mass customization [ • ETO construction lead time reduction [7] • Collaborative robots for shop-floor tasks [57] • Reduction in machine starvation and production losses [22] • Reduction in machinedependent electricity costs [37] • Agile scheduling in ETO construction [8] • Rescheduling efficiency [13] • PS recovery models for disruption management [19] Financial/ Market performance outcomes V23: Service levels V24: Cost of nonconformance V25: Quality ...
Improved flexibility, digitalization and informatization introduced by the he Fourth Industrial Revolution (or I4.0) has induced changes in the arrangement and execution of production scheduling (PS) activities. Prior research in this field is in the form of computational experiments specific to PS problem, while little is known about how digital capabilities influence PS and how it operates in this new paradigm. This paper reviews the literature on PS in I4.0 environments, conceptualizes it and develops a morphological analysis (MA) framework. We present the MA framework based on a literature review of 53 papers identified using a systematic review protocol. The framework constructed with five dimensions (i.e., study setting, enabling technologies, core performance outcomes, digital capabilities, and critical scheduling areas) and 49 associated variants reveal an upper bound of 636 research gaps as opportunities for future research. Further research directions are presented based on the main identified gaps. This paper is possibly the first to use MA for reviewing the PS literature, presenting practitioner and theoretical contributions. The MA framework acts as a ready reckoner of the literature and its modular representation has the flexibility to be modified or augmented as the literature evolves.
... Chakrabarty and Wang (2020) address variation in the stock market and inventory levels using cloud computing and autonomous vehicles, (Sharma and Kulkarni, 2016) use automation incorporating the MIMOSA database and Ontology-based knowledge repositories when proposing spare parts replenishment system framework. In engineering to order (ETO) Production Planning, (Dallasega et al., 2017) propose an information and communication technology (ICT)-supported nearly real-time capable production planning approach, which using a simulation, shows a drastic reduction of the inventory level on-site. In logistics processes, (Busert and Fay, 2019) adopt interfaces of the IT systems to control value stream logistics mapping, (Knoll et al., 2019)propose a methodology that combines multidimensional process mining (MDPM) techniques with proven principles of lean production and value stream mapping (VSM) using existing event data by automatically mapping physical logistics activities. ...
Purpose
This study aims to address Industry 4.0 (I4.0) technologies that can improve the research and implementation of lean supply chain management (LSCM) and the enhanced LSCM subfields in I4.0 technologies.
Design/methodology/approach
The authors conducted a systematic literature review to detect, categorize and assess recent data, highlighting patterns and providing suggestions for potential research in this field, to investigate I4.0 literature and its effect on LSCM. The authors examined 79 published types of research from the Scopus database that were published between 2010 and 2021 and classified them into four LSCM fields: logistics, production, supply chain and marketing.
Findings
The authors can emphasize the fact that the literature on this topic is in progress, from early German academic research to the current creation of new effects around the world. The majority of the potential effects investigated were discovered to improve specific areas that ultimately enhance the practices of the four LSCM domains as well as performance outcomes. The authors were also able to assess the extent to which present and upcoming I4.0 technologies can improve LSCM research and implementation.
Originality/value
To the best of the authors’ knowledge, this is the first study of its kind. Although some research looked into various areas of I4.0 and LSCM topics, there has been no research specifically looking into the impact of I4.0 on LSCM. The originality of this study lies in the treatment of the main fields and sub-fields of LSCM, which can benefit from the technologies of I4.0. Academic scholars interested in the research topics may benefit from the findings of this study. Organizations in various industrial sectors, particularly manufacturing, where lean thinking is used, business professionals specialized in lean operations and supply chain management, along with anyone else who wants to learn more about the interrelationships between I4.0 and LSCM.
... A stream of recent PPC research is focused on probing the limitations of conventional ERP systems in supporting real-time PPC decisions in complex manufacturing environments such as make-to-order setups (Laili et al., 2019;Xu & Chen 2018), small and medium enterprises and dynamic markets (Horstkemper & Hellingrath, 2016;Jiang et al., 2017). Some of the most stated shortcomings are about data quality and completeness for S&OP (Schlegel, 2020), synchronization of scheduling, MPS and MRP due to growing needs for product customization (Dallasega et al., 2017;Ke et al., 2019), and data diversity owing to multiple sources (Frank et al., 2019). Another stream of literature examines the decision support and auxiliary software systems such as manufacturing executive systems (MES) and advanced planning and scheduling (APS) systems for PPC in large and complex manufacturing environments (Grundstein et al., 2017;Malik & Kim, 2020). ...
... After excluding these intersecting cells signifying existing research, we were left with 372 pairs. Although each unexplored variant pair out of these 372 represented a potential research gap, it is the researchers' judgment that is critical to ascertain the research potential of the gap represented by a variant pair, D1: Study setting • fuzzy controller for realtime assembly line balancing; Huo et al., 2020) • Planning in ETO construction (Dallasega et al., 2017) • Scheduling in JIT environment (Xu & Chen, 2018) • Order allocation flexibility in CPPS (Horstkemper & Hellingrath, 2016) • Tracing intercell material movement in cellular manufacturing system (Li et al., 2016) • Vertical/ horizontal synchronization of PPC with ERP system (Gölzer & Fritzsche, 2017) • Autonomous production control method (Grundstein et al., 2017) • Resource-aware scheduling scheme (Malik & Kim 2020) • Scheduling-asa-service (Hao et al., (Qiao et al., 2020) • Event-driven production planning (Okpoti et al. 2021) • Reduction in machine starvation and production losses • Internal failure cost reduction (Hao et al., 2019) • Schedule stability (Fera et al., 2020) • Distributed customer orders by due dates (Chergui et al., 2018) • Collaborative robots for shop-floor tasks (Darwish et al. 2018) • Synchronization for ETO construction project (Dallasega et al., 2019) (Schlegel et al., 2020;Kumar et al., 2018). Another potential research gap is the development and evaluation of new dispatching rules (or metaheuristics) for meeting the cost and time optimization issues of the AM environment. ...
... Most of this research is on AI-based heuristics for scheduling in dynamic manufacturing and remanufacturing environments with uncertain disturbances such as Just-in-time (Xu & Chen, 2018), CPPS-based production (Horstkemper & Hellingrath, 2016) and other high-mix low-volume (HMLV) settings (Kocsi et al., 2020). Digitization of PPC for supporting the complex resource allocation (e.g., IoTenabled rescheduling models for dealing with uncertain job arrivals and machine breakdowns; Ghaleb et al. 2020), decentralized master scheduling (production planning in ETO construction; Dallasega et al., 2017) and other shop floor control (e.g., fuzzy controller for assembly line balancing; Huo et al., 2020) decisions in real-time are the second most explored digital capability (14 studies). Our analysis shows that the least investigated digital capabilities from the specific PPC activity standpoint are traceability & visibility of information and products (1 study from entire PPC perspective on tracing intercell material movement in cellular manufacturing system; Li et al., 2016) and scalability & reconfiguration (1 study on scalability/modularity of PPC function; Yu & Zhang, 2018), as indicated as RT-3 in Table 2. ...
The rapid industrial changes triggered by the fourth industrial revolution or I4.0 have ensued a dynamic and integrative perspective in production planning and control (PPC) decisions. Prior research on PPC transformation in I4.0 environment is in the form of computational experiments to solve sector-specific issues, with little emphasis on how PPC is influenced by the evolving digital capabilities, and how it operates in the new manufacturing paradigm? This study aims to develop an analytical framework to consolidate the prevailing research on PPC in I4.0 and identify future research possibilities. We used morphological analysis (MA) approach for reviewing 104 studies identified through a systematic review protocol. The MA framework is created with five dimensions (i.e., study setting, PPC functions, enabling technologies, digital capabilities, and core performance outcomes) defined based on input-process-outcome (IPO) approach of system analysis. This study is possibly the first to use MA for reviewing the PPC literature, presenting practitioner and theoretical contributions. The MA framework acts as a ready reckoner of the literature and its modular representation has the flexibility to be modified or augmented as the literature evolves.
... Manufacturers can sense deviations from the production plan when they appear and identify delays in the logistics network in real time. Nevertheless, a significant issue remains in the digital, sensing, and smart factory of the future, i.e., how digital integration and real-time data monitoring can be realized in practice [6,7]. Self-organization and self-optimization are aspects typically subsumed under the term I4.0. ...
... Works focusing on planning, with gradual application, were presented in Ud, Henskens, Paul, and Wallis [39] and Dallasega, Rojas, Rauch, and Matt [7]; both studies made it clear that the coverage of their application was limited and needed further deployment at the factory level. Ud, Henskens, Paul, and Wallis [39] presented a scheme for implementing I4.0 in SMEs called AOSF (agentoriented smart factory framework). ...
... Implementing the plant side and multiple dimensions of the user side was also intentionally left for an upcoming development in this particular project. Dallasega, Rojas, Rauch, and Matt [7] proposed an ICT-supported, nearly real-time-capable production planning approach, which utilized simulation and showed a drastic reduction in the on-site inventory level. The new pull model reduces the average buffer content by approximately 30% vs. working on the pull scheme. ...
Nowadays, Industry 4.0 (I4.0) has become a trendy topic in manufacturing industries worldwide. The definition is far from being comprehensible for small players, and the practical uptake is ambiguous. Transnational companies are often at the top in deploying I4.0 features, learning primarily from their experimentation. Alternatively, small- to medium-sized enterprises (SMEs), given their less stable value chains and unsteady processes, tend to target most of their efforts on controlling disturbances and adopting solutions for deviation control. Such solutions can be features that set the path for SMEs to transition to I4.0. This paper aims to examine the reported degree of digitalization in implemented solutions in SMEs when handling deviations and analyze the integration of such solutions in their digital transformation process. Systematic literature review (SLR) is used to examine literature published up to and including January 2019. The results show a higher concentration on practical applications rather than on frameworks. Existing frameworks that focus on SMEs address particular elements of I4.0 rather than a gradual shift with a holistic view, increasing the deployment difficulty for SMEs. This paper identifies potential constraints in deployment if such a trend maintains for consecutive years.
... Additionally, a value stream analysis based on a Big Data model [67] and the combination of VSM with a simulation [68] are described in the literature. More associated with the development and testing of countermeasures, a publication mentions scheduling solutions based on real-time simulations [69]. Regarding implementation activities, multiple publications report developments in task organization through Kanban boards; namely a web-based board [70], a board operated with a smartphone [71], and a computer-aided task board that tracks a physical panel in real-time [72]. ...
... This requirement can be further supported by on-demand cloud computing resources that allow high-speed simulation analytics [85], as well as IoT to transmit data between machines and sensors to software tools [9]. Regarding practical use cases, this requirement is present in scheduling solutions based on both real-time simulations [69] and the combination of VSM with simulation models in order to validate current and future states, aiding in decision-making processes [68]. ...
... X X X [69] Scheduling solutions based on real-time simulations, allowing to reach on-demand production and JIT delivery. ...
Continuous improvement (CI) is a key component of lean manufacturing (LM), which is fundamental for organizations to remain competitive in an ever more challenging market. At present, the new industrial revolution, Industry 4.0 (I4.0), is taking place in the manufacturing and service markets, allowing more intelligent and automated processes to become a reality through innovative technologies. Not much research was found regarding a holistic application of I4.0′s technological concepts towards CI, which clarifies the potential for improving its effectiveness. This clearly indicates that research is needed regarding this subject. The present publication intends to close this research gap by studying the main I4.0 technological concepts and their possible application towards a typical CI process, establishing the requirements for such an approach. Based on that study, a conceptual approach is proposed (PDCA 4.0), depicting how I4.0 technological concepts should be used for CI enhancement, while aiming to satisfy the identified requirements. By outlining the PDCA 4.0 approach, this paper contributes to increasing the knowledge available regarding the CI realm on how to support the CI shift towards a I4.0 industrial paradigm.
... CPS capture data of the physical world via sensors, use the Internet, and cloud computing to communicate between the connectors, and interact with the physical world utilizing mechatronic actuators (Lee 2008;Zsifkovits and Woschank 2019). This enables autonomous control systems, which can satisfy customer demands in real-time (Spath et al. 2013;Dallasega et al. 2017). CPS, as well as the Internet of Things (IoT), allow enterprises to sense deviations from the production plan as soon as they appear and identify delays in real-time (Magoutas et al. 2014;Chaopaisarn and Woschank 2019). ...
The integrated planning and control of logistics processes can be seen as one of the basic prerequisites for the successful implementation of smart production systems and smart and lean supply chains, as well. Therefore, modern Industry 4.0 approaches are mainly focusing on (1) the principles of decentralization and (2) the usage of real-time data to improve the overall logistics performance in terms of promised delivery dates, work in progress, capacity utilization, and lead-times. In this context, this chapter systematically evaluates the application of decentralized production planning and control strategies, e.g., KANBAN and CONWIP, in comparison with traditional approaches, like MRP. Moreover, the impact of real-time data usage in production planning and control systems on lead-times and work in progress is investigated using a discrete event simulation based on primary data from a make to order manufacturer. The results of this industrial case study research confirm the significant potential that lies in smart production systems and smart and lean supply chains and, therefore, in the introduction of Industry 4.0 technologies and technological concepts in production and logistics systems.
... The technological solutions are often defined as nine elements referred to as "foundational technology advances" i.e. big data and analytics, simulation, autonomous robots, internet of things, cyber-physical systems (CPS), cloud computing, virtual reality, machine-to-machine communication, and cybersecurity [14]. A common practice for researchers and practitioners tends to be treating the technological solutions as standalone elements [5,[10][11][12][13]. However, to achieve a successful transformation, I4.0 as a whole should be well understood and a clear road map is to be generated and implemented (Lim et al., 2020). ...
... I4.0 projects driven by SMEs often remain cost-driven initiatives [5,11] and to this day there is no evidence of real business model transformation [5]. Empirical cases in research are frequently centered around presenting single applications, emphasizing the low-cost factor as a main advantage and strength of their application [3,[12][13][14][15][16][17]. The implementation of the I4.0 concept implies a major challenge which is resilience and robustness of the production system, which is the ability to absorb manufacturing disturbances without failing or breaking and be able to adapt to major variations and gradually return to its original state or "normal" state and level of performance [18,19]. ...
This paper proposes a conceptual implementation model for small and medium enterprises (SMEs) to follow as part of their digital transformation. The conceptual model can be translated into a practical step-by-step guide for SMEs to apply during their digital transformation. The model is based on gradually developing industrial capabilities that can influence production processes performance. We employed a comparative case study approach to capture the lessons learned by SMEs in their journey to develop and implement a production digitalization system for deviation management and performance improvement. The model was validated in the cases of study capturing the actual SMEs’ needs. Managerial capabilities of production processes such as monitoring and control demonstrate to influence the performance positively. The proposed model aims for a full digital transformation by following a gradual approach to being resource-efficient and integrating their business needs. This paper is an extension of work originally presented in APMS 2020, IFIP AICT 592.
